Leveraging Cyberattack News Tweets for Advanced Threat Detection and Classification Using Ensemble of Deep Learning Models With Wolverine Optimization Algorithm

At present, cyber-attacks have become more critical and familiar, which appeals to a novel line of security defences to defend against them. Cyber Threat Intelligence (CTI) originated as a reputation in the frequently developing cybersecurity landscape, vital in safeguarding digital models. Understanding this domain develops over a complete study of intelligence’s numerous features and sources. Data sharing and analysis centres perform as alarms of collaboration, demonstrating the collective vigilance needed to oppose such developing attacks. Inspecting and gathering data about cyberattacks from tweets can effectively deliver critical perceptions of the threats, their effects, occurrence areas, and probable mitigation tactics. Existing study on cyberattack absences in establishing Artificial Intelligence (AI) based analytic solutions for delivering country-wide cyber-attack intelligence. Cyber planners at a domestic level need AI-based decision support models to determine a country’s cyber attitude or vigilance. This study designs and develops an Enhanced Threat Intelligence for Cybersecurity Using an Ensemble of Deep Leaning Models with Metaheuristic Optimization Algorithm (ETIC-EDLMOA) model. The presented ETIC-EDLMOA model’s main aim is to detect and mitigate network attacks in cybersecurity effectively. Initially, the ETIC-EDLMOA model undergoes a data pre-processing stage to ensure clean and structured input data for analysis. Besides, the Word2vec model is utilized for feature extraction. For the classification process, the ensemble of DL models is employed, including the recurrent neural network (RNN) method, long short-term memory (LSTM) model, and conditional variational autoencoders (CVAE) technique. Finally, the ensemble models’ hyperparameter fine-tuning process is performed using the Wolverine optimization algorithm (WoOA) technique. A comprehensive range of simulation analyses is conducted to ensure the improved performance of the ETIC-EDLMOA method on the CybAttT dataset. The comparison study of the ETIC-EDLMOA method illustrated a superior accuracy value of 98.51% over existing techniques.